Abstract
Nowadays, Nonlinear Least-Squares embodies the foundation of many Robotics and Computer Vision systems. The research community deeply investigated this topic in the last few years, and this resulted in the development of several open-source solvers to approach constantly increasing classes of problems. In this work, we propose a unified methodology to design and develop efficient Least-Squares Optimization algorithms, focusing on the structures and patterns of each specific domain. Furthermore, we present a novel open-source optimization system that addresses problems transparently with a different structure and designed to be easy to extend. The system is written in modern C++ and runs efficiently on embedded systemsWe validated our approach by conducting comparative experiments on several problems using standard datasets. The results show that our system achieves state-of-the-art performances in all tested scenarios.
Highlights
Iterative Least-Squares (ILS) solvers are core building blocks of many robotic applications, systems, and subsystems [1]
We denote with Hi,j the block i, j of the H matrix corresponding to the variables Xi and X j ; we indicate with bi the block of the coefficient vector for the variable Xi
As support material for this tutorial, we offer an own implementation of a modular ILS
Summary
Iterative Least-Squares (ILS) solvers are core building blocks of many robotic applications, systems, and subsystems [1]. This technique has been traditionally used for calibration [2,3,4], registration [5,6,7], and global optimization [8,9,10,11]. Systems typically employ multiple ILS solvers at different levels: in computing the incremental ego-motion of the sensor, in refining the localization of a robot upon loop closure and—most notably—to obtain a globally consistent map. If the noise affecting the sensor data are Gaussian, the solution of a factor graph can be computed by an ILS solver implementing variants of the well known Gauss–Newton (GN) algorithm
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
